AI Health Contracts: 10 Questions for Health Plans

The promise of artificial intelligence in healthcare is undeniable, but for health plan executives and employers, navigating the landscape of AI-powered health solutions can feel like traversing a minefield of hype. With an influx of vendors claiming AI capabilities, distinguishing truly impactful, clinically validated platforms from those offering superficial enhancements is paramount. The question isn’t whether AI will transform healthcare, but rather how to identify the “AI-native” solutions that deliver tangible, evidence-based value. This demands a rigorous procurement process, one that moves beyond marketing claims to scrutinize the foundational elements that define an AI-native approach in a clinical context.

Defining AI-Native: Beyond the Buzzword

The term “AI-native” is often misused, applied broadly to any digital health tool incorporating even rudimentary algorithms. However, for a solution to be truly AI-native in a clinical setting, it must adhere to three core principles: it must be trained on real patient outcomes data, operate within defined clinical guardrails, and possess published evidence of efficacy. This strict definition serves as the bedrock for evaluating potential partners, ensuring that investments translate into improved member health and cost efficiencies.

Consider the varying approaches among prominent health technology companies. While innovators like Omada Health and Hinge Health have built robust digital platforms, and companies such as Spring Health and Noom leverage algorithmic approaches for specific interventions, their AI integration points and depth of AI-nativeness can differ significantly. Even established players like Teladoc Health are increasingly incorporating AI, but the distinction lies in whether AI is a bolt-on feature or the fundamental architecture of the solution. Hemant Taneja, a vocal proponent of AI’s transformative potential in healthcare, has often emphasized the need for platforms built from the ground up to harness AI effectively, rather than merely layering it onto existing systems. Similarly, the insights of figures like Karen DeSalvo, with her deep understanding of health information technology, underscore the importance of responsible and evidence-based AI deployment in clinical workflows.

The AI-Native Procurement Checklist: 10 Critical Questions

To effectively evaluate AI health solutions, Health Plan Executives (A2) and Employers/HR (A3) must ask pointed questions that cut through the noise. This checklist provides a framework for robust due diligence:

1. Data Provenance: How was the AI model trained? Is it exclusively on real patient outcomes data, and is that data diverse and representative of your member population?
2. Clinical Guardrails: What explicit clinical guardrails are embedded within the AI to ensure patient safety and ethical practice? How are these guardrails monitored and updated?
3. Published Evidence of Efficacy: Can the vendor provide peer-reviewed publications or robust internal studies demonstrating the AI’s efficacy in improving patient outcomes, reducing costs, or both? Example of a peer-reviewed study on AI efficacy in healthcare
4. FDA Status: Does the AI solution require FDA clearance (e.g., as a Software as a Medical Device, or SaMD)? If so, what is its current status (e.g., 510(k) clearance, De Novo classification, or Premarket Approval (PMA))?
5. HIPAA Compliance: How does the solution ensure strict adherence to HIPAA regulations for patient data privacy and security? What additional certifications (e.g., HITRUST, SOC 2 Type II) are in place?
6. Return on Investment (ROI): What specific, measurable ROI metrics can the vendor provide, backed by real-world evidence (RWE), that demonstrate value for health plans or employer groups?
7. Scalability: Is the AI platform designed to scale efficiently across large populations without compromising performance or clinical integrity?
8. Interoperability: How seamlessly does the AI solution integrate with existing electronic health record (EHR) systems and other healthcare IT infrastructure (e.g., through FHIR-based APIs and USCDI standards)?
9. Human Oversight and Intervention: What mechanisms are in place for human clinicians to oversee, validate, and, if necessary, override AI-generated recommendations or actions?
10. Exit Provisions and Data Portability: What are the contractual terms for data portability and system disengagement should the partnership conclude?

Companies like Commure, focused on foundational healthcare infrastructure, understand the complexity of building interoperable and secure systems that can host AI-native applications. When evaluating solutions from companies like Omada Health, Hinge Health, Spring Health, Noom, or Teladoc Health, applying these questions rigorously will reveal the true depth of their AI capabilities and their commitment to clinical rigor.

Navigating the Regulatory and Industry Landscape

The procurement of AI-native health solutions does not occur in a vacuum. Health Plan Executives (A2) and Employers/HR (A3) must consider the broader regulatory and industry environment. Adherence to HIPAA is non-negotiable for any health technology dealing with protected health information. Furthermore, evolving standards, particularly those emphasizing FHIR-based APIs and USCDI, underscore the critical need for interoperability, ensuring that AI solutions can effectively exchange information within a complex healthcare ecosystem. For health plans, alignment with NCQA Standards, including the recently launched Digital Health Engagement Accreditation program, is crucial, impacting accreditation and quality measures. NCQA standards for digital health tools

Organizations like AHIP provide valuable guidance and advocacy for health plans navigating new technologies, while NCQA sets benchmarks for quality and performance. Independent Review Organizations (IROs) and employer coalitions are also increasingly scrutinizing AI solutions, demanding transparency and verifiable outcomes. These bodies collectively reinforce the need for AI-native solutions that are not only innovative but also compliant, effective, and trustworthy. The emphasis is shifting from simply having AI to having AI that is demonstrably safe, effective, and integrated responsibly into care pathways, generating real-world evidence that stands up to scrutiny.

The Imperative for Rigorous Evaluation

In an era where “AI” can mean anything from advanced predictive analytics to basic automation, a precise definition of “AI-native” is essential for health plans and employers. Investing in solutions that are truly trained on real patient outcomes data, operate within defined clinical guardrails, and provide published evidence of efficacy is not merely a best practice; it is a strategic imperative. The 10-question procurement checklist provides a robust framework for this evaluation, ensuring that partnerships with companies like Commure, Omada Health, Hinge Health, Spring Health, Noom, and Teladoc Health are grounded in clinical validity and deliver measurable value. By adopting this rigorous approach, stakeholders can confidently harness the transformative power of AI to improve health outcomes, enhance efficiency, and ultimately, redefine the future of healthcare delivery. White paper on AI in healthcare procurement for health plans